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The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%. [ABSTRACT FROM AUTHOR]

The density and uniformity of lidar data play crucial roles in the corresponding data processing steps. One factor influencing point density and spacing in lidar data is the presence of empty pulses, where no return is detected. Missing returns can occur due to atmospheric absorption, specular and diffusive reflection, etc. To address this issue and enhance point density, this paper introduces a novel method for approximating missing returns in airborne lidar data collected over urban areas. This technique focuses on approximating returns for empty pulses that hit spots near abrupt slope changes on building and ground surfaces. The proposed methodology is validated through experiments using a lidar data set from downtown Dublin, Ireland. The collected data contained numerous gaps associated with wet surfaces, as well as missing returns on vertical and oblique surfaces. [ABSTRACT FROM AUTHOR]

The state of Indiana maintains approximately 120 salt storage facilities strategically distributed across the state for winter operations. In April 2023, those facilities contained approximately 217,000 tons of salt with an estimated value of USD 21 million. Accurate inventories at each facility during the winter season are important for scheduling re-supply so the facilities do not run out of salt. Inventories are also important at the end of the season for restocking to provide balanced inventories. This paper describes the implementation of a portable pole-mounted LiDAR system to measure salt stockpile inventory at 120 salt storage facilities in Indiana. Using two INDOT staff members, the end-of-season inventory took 9 working days, with volumetric inventories provided within 24 h of data collection. To provide an independent evaluation of the methodologies, the Hovermap ST backpack was used at selected facilities to provide control volumes. This system has a range of 100 m and an accuracy of ±3 cm, which reduces the occlusion to less than 8%. The pre-season facility capacity ranged from 0% to 100%, with an average of 66% full across all facilities. The post-season facility percentage ranged from 3% to 100%, with an average of 70% full. In addition, permanent roof-mounted LiDAR systems were deployed at two facilities to evaluate the effectiveness of monitoring salt stockpile inventories during winter operation activities. Plans are now underway to install fixed LiDAR systems at 15 additional facilities for the 2023–2024 winter season. [ABSTRACT FROM AUTHOR]

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support longstanding principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing. [ABSTRACT FROM AUTHOR]

Forest inventory has been relying on labor-intensive manual measurements. Using remote sensing modalities for forest inventory has gained increasing attention in the last few decades. However, tools for deriving accurate tree-level metrics are limited. This paper investigates the feasibility of using LiDAR units onboard uncrewed aerial vehicle (UAV) and Backpack mobile mapping systems (MMSs) equipped with an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) to provide high-quality point clouds for accurate, fine-resolution forest inventory. To improve the quality of the acquired point clouds, a system-driven strategy for mounting parameters estimation and trajectory enhancement using terrain patches and tree trunks is proposed. By minimizing observed discrepancies among conjugate features captured at different timestamps from multiple tracks by single/multiple systems, while considering the absolute and relative positional/rotational information provided by the GNSS/INS trajectory, system calibration parameters and trajectory information can be refined. Furthermore, some forest inventory metrics, such as tree trunk radius and orientation, are derived in the process. To evaluate the performance of the proposed strategy, three UAV and two Backpack datasets covering young and mature plantations were used in this study. Through sequential system calibration and trajectory enhancement, the spatial accuracy of the UAV point clouds improved from 20 cm to 5 cm. For the Backpack datasets, when the initial trajectory was of reasonable quality, conducting trajectory enhancement significantly improved the relative alignment of the point cloud from 30 cm to 3 cm, and an absolute accuracy at the 10 cm level can be achieved. For a lower-quality trajectory, the initial 1 m misalignment of the Backpack point cloud was reduced to 6 cm through trajectory enhancement. However, to derive products with accurate absolute accuracy, UAV point cloud is required as a reference in the trajectory enhancement process of the Backpack dataset. [ABSTRACT FROM AUTHOR]

The utilization of remote sensing technologies for archaeology was motivated by their ability to map large areas within a short time at a reasonable cost. With recent advances in platform and sensing technologies, uncrewed aerial vehicles (UAV) equipped with imaging and Light Detection and Ranging (LiDAR) systems have emerged as a promising tool due to their low cost, ease of deployment/operation, and ability to provide high-resolution geospatial data. In some cases, archaeological sites might be covered with vegetation, which makes the identification of below-canopy structures quite challenging. The ability of LiDAR energy to travel through gaps within vegetation allows for the derivation of returns from hidden structures below the canopy. This study deals with the development and deployment of a UAV system equipped with imaging and LiDAR sensing technologies assisted by an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) for the archaeological mapping of Dana Island, Turkey. Data processing strategies are also introduced for the detection and visualization of underground structures. More specifically, a strategy has been developed for the robust identification of ground/terrain surface in a site characterized by steep slopes and dense vegetation, as well as the presence of numerous underground structures. The derived terrain surface is then used for the automated detection/localization of underground structures, which are then visualized through a web portal. The proposed strategy has shown a promising detection ability with an F1-score of approximately 92%. [ABSTRACT FROM AUTHOR]

Stockpile volume estimation plays a critical role in several industrial/commercial bulk material management applications. LiDAR systems are commonly used for this task. Thanks to Global Navigation Satellite System (GNSS) signal availability in outdoor environments, Uncrewed Aerial Vehicles (UAV) equipped with LiDAR are frequently adopted for the derivation of dense point clouds, which can be used for stockpile volume estimation. For indoor facilities, static LiDAR scanners are usually used for the acquisition of point clouds from multiple locations. Acquired point clouds are then registered to a common reference frame. Registration of such point clouds can be established through the deployment of registration targets, which is not practical for scalable implementation. For scans in facilities bounded by planar walls/roofs, features can be automatically extracted/matched and used for the registration process. However, monitoring stockpiles stored in dome facilities remains to be a challenging task. This study introduces an image-aided fine registration strategy of acquired sparse point clouds in dome facilities, where roof and roof stringers are extracted, matched, and modeled as quadratic surfaces and curves. These features are then used in a Least Squares Adjustment (LSA) procedure to derive well-aligned LiDAR point clouds. Planar features, if available, can also be used in the registration process. Registered point clouds can then be used for accurate volume estimation of stockpiles. The proposed approach is evaluated using datasets acquired by a recently developed camera-assisted LiDAR mapping platform—Stockpile Monitoring and Reporting Technology (SMART). Experimental results from three datasets indicate the capability of the proposed approach in producing well-aligned point clouds acquired inside dome facilities, with a feature fitting error in the 0.03–0.08 m range. [ABSTRACT FROM AUTHOR]

Forests are the main part of the terrestrial ecosystem. Airborne LiDAR is fast, comprehensive, penetrating, and contactless and can depict 3D canopy information with a high efficiency and accuracy. Therefore, it plays an important role in forest ecological protection, tree species recognition, carbon sink calculation, etc. Accurate recognition of individual trees in forests is a key step to various application. In real practice, however, the accuracy of individual tree segmentation (ITS) is often compromised by under-segmentation due to the diverse species, obstruction and understory trees typical of a high-density multistoried mixed forest area. Therefore, this paper proposes an ITS optimization method based on Gaussian mixture model for airborne LiDAR data. First, the mean shift (MS) algorithm is used for the initial ITS of the pre-processed airborne LiDAR data. Next, under-segmented samples are extracted by integrated learning, normally segmented samples are classified by morphological approximation, and the approximate distribution uncertainty of the normal samples is described with a covariance matrix. Finally, the class composition among the under-segmented samples is determined, and the under-segmented samples are re-segmented using Gaussian mixture model (GMM) clustering, in light of the optimal covariance matrix of the corresponding categories. Experiments with two datasets, Trento and Qingdao, resulted in ITS recall of 94% and 96%, accuracy of 82% and 91%, and F-scores of 0.87 and 0.93. Compared with the MS algorithm, our method is more accurate and less likely to under-segment individual trees in many cases. It can provide data support for the management and conservation of high-density multistoried mixed forest areas. [ABSTRACT FROM AUTHOR]

Transportation agencies in northern environments spend a considerable amount of their budget on salt for winter operations. For example, in the state of Indiana, there are approximately 140 salt storage facilities distributed throughout the state and the state expends between USD 30 M and USD 60 M on inventory and delivery each year. Historical techniques of relying on visual estimates of salt stockpiles can be inaccurate and do not scale well for managing the supply chain during the winter or planning for re-supply during summer months. This paper describes the implementation of a portable pole mounted LiDAR system that can be used to inventory a large barn in under 15 min and describes how this system has been deployed over 90 times at 30 facilities. A quick and easy accuracy test, based upon conservation of volume, was used to provide an independent check on the system performance by repositioning portions of the salt pile. Those tests indicated stockpile volumes can be estimated with an accuracy of approximately 0.1%. The paper concludes by discussing how this technology can be permanently installed near the roof for systematic monitoring throughout the year. [ABSTRACT FROM AUTHOR]

Building occlusions usually decreases the accuracy of boundary regularization. Thus, it is essential that modeling methods address this problem, aiming to minimize its effects. In this context, we propose a weighted iterative changeable degree spline (WICDS) approach. The idea is to use a weight function for initial building boundary points, assigning a lower weight to the points in the occlusion region. As a contribution, the proposed method allows the minimization of errors caused by the occlusions, resulting in a more accurate contour modeling. The conducted experiments are performed using both simulated and real data. In general, the results indicate the potential of the WICDS approach to model a building boundary with occlusions, including curved boundary segments. In terms of Fscore and PoLiS, the proposed approach presents values around 99% and 0.19 m, respectively. Compared with the previous iterative changeable degree spline (ICDS), the WICDS resulted in an improvement of around 6.5% for completeness, 4% for Fscore, and 0.24 m for the PoLiS metric. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS) have been utilized to detect lane markings through intensity-based approaches. As LiDAR data continue to be used for lane marking extraction, greater emphasis is being placed on enhancing the utility of the intensity values. Typically, intensity correction/normalization approaches are conducted prior to lane marking extraction. The goal of intensity correction is to adjust the intensity values of a LiDAR unit using geometric scanning parameters (i.e., range or incidence angle). Intensity normalization aims at adjusting the intensity readings of a LiDAR unit based on the assumption that intensity values across laser beams/LiDAR units/MMS should be similar for the same object. As MMS technology develops, correcting/normalizing intensity values across different LiDAR units on the same system and/or different MMS is necessary for lane marking extraction. This study proposes a generalized correction/normalization approach for handling single-beam/multi-beam LiDAR scanners onboard single or multiple MMS. The generalized approach is developed while considering the intensity values of asphalt and concrete pavement. For a performance evaluation of the proposed approach, geometric/morphological and deep/transfer-learning-based lane marking extraction with and without intensity correction/normalization is conducted. The evaluation shows that the proposed approach improves the performance of lane marking extraction (e.g., the F1-score of a U-net model can change from 0.1% to 86.2%). [ABSTRACT FROM AUTHOR]

LiDAR data acquired by various platforms provide unprecedented data for forest inventory and management. Among its applications, individual tree detection and segmentation are critical and prerequisite steps for deriving forest structural metrics, especially at the stand level. Although there are various tree detection and localization approaches, a comparative analysis of their performance on LiDAR data with different characteristics remains to be explored. In this study, a new trunk-based tree detection and localization approach (namely, height-difference-based) is proposed and compared to two state-of-the-art strategies—DBSCAN-based and height/density-based approaches. Leaf-off LiDAR data from two unmanned aerial vehicles (UAVs) and Geiger mode system with different point densities, geometric accuracies, and environmental complexities were used to evaluate the performance of these approaches in a forest plantation. The results from the UAV datasets suggest that DBSCAN-based and height/density-based approaches perform well in tree detection (F1 score > 0.99) and localization (with an accuracy of 0.1 m for point clouds with high geometric accuracy) after fine-tuning the model thresholds; however, the processing time of the latter is much shorter. Even though our new height-difference-based approach introduces more false positives, it obtains a high tree detection rate from UAV datasets without fine-tuning model thresholds. However, due to the limitations of the algorithm, the tree localization accuracy is worse than that of the other two approaches. On the other hand, the results from the Geiger mode dataset with low point density show that the performance of all approaches dramatically deteriorates. Among them, the proposed height-difference-based approach results in the greatest number of true positives and highest F1 score, making it the most suitable approach for low-density point clouds without the need for parameter/threshold fine-tuning. [ABSTRACT FROM AUTHOR]

This article proposes a solution to special least squares adjustment (LSA) models with a rank-deficient weight matrix, which are commonly encountered in geomatics. The two sources of rank deficiency in weight matrices are discussed: naturally occurring due to the inherent characteristics of LSA mathematical models and artificially induced to eliminate nuisance parameters from LSA estimation. The physical interpretation of the sources of rank deficiency is demonstrated using a case study to solve the problem of 3D line fitting, which is often encountered in geomatics but has not been addressed fully to date. Finally, some geomatics-related applications--mobile lidar system calibration, point cloud registration, and single-photo resection--are discussed along with respective experimental results, to emphasize the need to assess LSA models and their weight matrices to draw inferences regarding the effective contribution of observations. The discussion and results demonstrate the vast applications of this research in geomatics as well as other engineering domains. [ABSTRACT FROM AUTHOR]

Lane width evaluation is one of the crucial aspects in road safety inspection, especially in work zones where a narrow lane width can result in a reduced roadway capacity and also, increase the probability of severe accidents. Using mobile mapping systems (MMS) equipped with laser scanners is a safe and cost-effective method for rapidly collecting detailed information along road surface. This paper presents an approach to derive lane width estimates using point clouds acquired from a geometrically-calibrated mobile mapping system. Starting from an accurate LiDAR point cloud, the road surface is extracted with the assistance of trajectory elevation data. Lane markings are identified based on the intensity data. Next, the lane marking centerline is derived and clustered to identify areas with ambiguous or missing lane markings and finally, use the normal (or, unambiguous) lane markings to estimate the lane width. The derived lane width estimates are used to develop a reporting mechanism for areas with narrow lanes, ambiguous lane markings, missing lane markings, and/or wide lanes. [ABSTRACT FROM AUTHOR]

Mechanically stabilized earth (MSE) walls rely on its weight to resist the destabilizing earth forces acting at the back of the reinforced soil area. MSE walls are a common infrastructure along national and international transportation corridors as they are low-cost and have easy-to-install precast concrete panels. The usability of such transportation corridors depends on the safety and condition of the MSE wall system. Consequently, MSE walls have to be periodically monitored according to prevailing transportation asset management criteria during the construction and serviceability life stages to ensure that their predictable performance measures are met. To date, MSE walls are monitored using qualitative approaches such as visual inspection, which provide limited information. Aside from being time-consuming, visual inspection is susceptible to bias due to human subjectivity. Manual and visual inspection in the field has been traditionally based on the use of a total station, geotechnical field instrumentation, and/or static terrestrial laser scanning (TLS). These instruments can provide highly accurate and reliable performance measures; however, their underlying data acquisition and processing strategies are time-consuming and not scalable. The proposed strategy in this research provides several global and local serviceability measures through efficient processing of point cloud data acquired by a mobile LiDAR system (MLS) for MSE walls with smooth panels without the need for installing any targets. An ultra-high-accuracy vehicle-based LiDAR data acquisition system has been used for the data acquisition. To check the viability of the proposed methodology, a case study has been conducted to evaluate the similarity of the derived serviceability measures from TLS and MLS technologies. The results of that comparison verified that the MLS-based serviceability measures are within 1 cm and 0.3° of those obtained using TLS and thus confirmed the potential for using MLS to efficiently acquire point clouds while facilitating economical, scalable, and reliable monitoring of MSE walls. [ABSTRACT FROM AUTHOR]

This paper proposes a multiunit light detection and ranging (LiDAR) system calibration procedure to directly estimate the mounting parameters relating multiple spinning multibeam laser scanners to the global navigation satellite system/inertial navigation system (GNSS/INS) unit onboard a mobile terrestrial platform in order to derive point clouds with high-positional accuracy. This procedure is based on the use of conjugate planar/linear features in overlapping point clouds derived from different drive runs. In order to increase the efficiency of semiautomatic conjugate feature extraction from LiDAR data, specifically designed calibration boards covered by highly reflective surfaces that could be easily deployed and set up within outdoor environments are used in this paper. To ensure the accuracy of the estimated mounting parameters, an optimal configuration of target primitives and drive runs is determined by analyzing the potential impact of bias in mounting parameters of a LiDAR unit on the resultant point cloud for different orientations of target primitives and different drive run scenarios. This impact is also verified experimentally by simulating a bias in each mounting parameter separately. Finally, the optimal configuration is used within an experimental setup to evaluate the performance of the proposed calibration procedure through the a posteriori variance factor of least squares adjustment and the quality of fit of adjusted point cloud to linear/planar surfaces before and after the calibration process. The proposed calibration approach attained an accuracy of 1.42 cm, which is better than the accuracy expected based on the specifications of the involved hardware components, i.e., the LiDAR and GNSS/INS units. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) is a technology that uses laser beams to measure ranges and generates precise 3D information about the scanned area. It is rapidly gaining popularity due to its contribution to a variety of applications such as Digital Building Model (DBM) generation, telecommunications, infrastructure monitoring, transportation corridor asset management and crash/accident scene reconstruction. To derive point clouds with high positional accuracy, estimation of mounting parameters relating the laser scanners to the onboard Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) unit, i.e., the lever-arm and boresight angles, is the foremost and necessary step. This paper proposes a LiDAR system calibration strategy for a Unmanned Aerial Vehicle (UAV)-based mobile mapping system that can directly estimate the mounting parameters for spinning multi-beam laser scanners through an outdoor calibration procedure. This approach is based on the use of conjugate planar/linear features in overlapping point clouds derived from different flight lines. Designing an optimal configuration for calibration is the first and foremost step in order to ensure the most accurate estimates of mounting parameters. This is achieved by conducting a rigorous theoretical analysis of the potential impact of bias in mounting parameters of a LiDAR unit on the resultant point cloud. The dependency of the impact on the orientation of target primitives and relative flight line configuration would help in deducing the configuration that would maximize as well as decouple the impact of bias in each mounting parameter so as to ensure their accurate estimation. Finally, the proposed analysis and calibration strategy are validated by calibrating a UAV-based LiDAR system using two different datasets-one acquired with flight lines at a single flying height and the other with flight lines at two different flying heights. The calibration performance is evaluated by analyzing correlation between the estimated system parameters, the a-posteriori variance factor of the Least Squares Adjustment (LSA) procedure and the quality of fit of the adjusted point cloud to planar/linear features before and after the calibration process. [ABSTRACT FROM AUTHOR]

Abstract: Three-dimensional building models are important for various applications, such as disaster management and urban planning. The development of laser scanning sensor technologies has resulted in many different approaches for efficient building model generation using LiDAR data. Despite this effort, generation of these models lacks economical and reliable techniques that fully exploit the advantage of LiDAR data. Therefore, this research aims to develop a framework for fully-automated building model generation by integrating data-driven and model-driven methods using LiDAR datasets. The building model generation starts by employing LiDAR data for building detection and approximate boundary determination. The generated building boundaries are then integrated into a model-based processing strategy because LiDAR derived planes show irregular boundaries due to the nature of LiDAR point acquisition. The focus of the research is generating models for the buildings with right-angled-corners, which can be described with a collection of rectangles under the assumption that the majority of the buildings in urban areas belong to this category. Therefore, by applying the Minimum Bounding Rectangle (MBR) algorithm recursively, the LiDAR boundaries are decomposed into sets of rectangles for further processing. At the same time, the quality of the MBRs is examined to verify that the buildings, from which the boundaries are generated, are buildings with right-angled-corners. The parameters that define the model primitives are adjusted through a model-based boundary fitting procedure using LiDAR boundaries. The level of details in the final Digital Building Model is based on the number of recursions during the MBR processing, which in turn are determined by the LiDAR point density. The model-based boundary fitting improves the quality of the generated boundaries and as seen in experimental results, the quality depends on the average LiDAR point spacing. This research thus develops an approach which not only automates the building model generation, but also achieves the best accuracy of the model while utilizing only LiDAR data. [Copyright &y& Elsevier]

LiDAR technology is rapidly evolving as various new systems emerge, providing unprecedented data to characterize forest vertical structure. Data from different LiDAR systems present distinct characteristics owing to a combined effect of sensor specifications, data acquisition strategies, as well as forest conditions such as tree density and canopy cover. Comparative analysis of multi-platform, multi-resolution, and multi-temporal LiDAR data provides guidelines for selecting appropriate LiDAR systems and data processing tools for different research questions, and thus is of crucial importance. This study presents a comprehensive comparison of point clouds from four systems, linear and Geiger-mode LiDAR from manned aircraft and multi-beam LiDAR on unmanned aerial vehicle (UAV), and in-house developed Backpack, with the consideration of different forest canopy cover scenarios. The results suggest that the proximal Backpack LiDAR can provide the finest level of information, followed by UAV LiDAR, Geiger-mode LiDAR, and linear LiDAR. The emerging Geiger-mode LiDAR can capture a significantly higher level of detail while operating at a higher altitude as compared to the traditional linear LiDAR. The results also show: (1) canopy cover percentage has a critical impact on the ability of aerial and terrestrial systems to acquire information corresponding to the lower and upper portions of the tree canopy, respectively; (2) all the systems can obtain adequate ground points for digital terrain model generation irrespective of canopy cover conditions; and (3) point clouds from different systems are in agreement within a ±3 cm and ±7 cm range along the vertical and planimetric directions, respectively. [ABSTRACT FROM AUTHOR]

This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (DDWI). A new technique based on the buffer overlay method is also presented to determine the shoreline changes from different satellite images and obtain a time series for the shoreline changes. The shoreline detection model was applied to imagery from multiple satellites and validated to have sub-pixel accuracy using beach survey data that were collected from the Lake Michigan (USA) shoreline using a novel backpack-based LiDAR system. The model was also applied to 132 satellite images of a Lake Michigan beach over a three-year period and detected the shoreline accurately, with a >99% success rate. The model out-performed other existing shoreline detection algorithms based on different water indices and clustering techniques. The resolution shoreline position timeseries is the first satellite image-extracted dataset of its kind in terms of its high spatial and temporal resolution, and paves the road to obtaining other high-temporal-resolution datasets to refine models of beaches worldwide. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) has demonstrated its capabilities as a prominent technique for the direct acquisition of high density and accurate topographic information. To achieve the potential accuracy of such systems, a rigorous system calibration should be performed. We introduce a novel rigorous LiDAR system calibration procedure where the system parameters are determined by minimizing the discrepancies among conjugate surface elements in overlapping strips and control data, if available. The method is automated and does not require specific features (e.g., planes or lines) in the covered area or preclassification of the point cloud. Suitable primitives, which do not involve preprocessing of the data, are implemented. The correspondence between conjugate primitives is determined using a robust matching procedure. A modification to the Gauss Markov model is introduced to keep the implementation of the calibration procedure simple while utilizing higher order primitives. Experimental results have demonstrated the effectiveness of the proposed method over different types of terrain coverage. [ABSTRACT FROM AUTHOR]

LiDAR (Light Detection And Ranging) systems are capable of providing 3D positional and spectral information (in the utilized spectrum range) of the mapped surface. Due to systematic errors in the system parameters and measurements, LiDAR systems require geometric calibration and radiometric correction of the intensity data in order to maximize the benefit from the collected positional and spectral information. This paper presents a practical approach for the geometric calibration of LiDAR systems and radiometric correction of collected intensity data while investigating their impact on the quality of the derived products. The proposed approach includes the use of a quasi-rigorous geometric calibration and the radar equation for the radiometric correction of intensity data. The proposed quasi-rigorous calibration procedure requires time-tagged point cloud and trajectory position data, which are available to most of the data users. The paper presents a methodology for evaluating the impact of the geometric calibration on the relative and absolute accuracy of the LiDAR point cloud. Furthermore, the impact of the geometric calibration and radiometric correction on land cover classification accuracy is investigated. The feasibility of the proposed methods and their impact on the derived products are demonstrated through experimental results using real data. [ABSTRACT FROM AUTHOR]

Lidar (laser scanning) technology has been proven as a prominent technique for the acquisition of high-density and accurate topographic information. Because of systematic errors in the lidar measurements (drifts in the position and orientation information and biases in the mirror angles and ranges) and/or in the parameters relating the system components (mounting parameters), adjacent lidar strips may exhibit discrepancies. Although position and orientation drifts can have a more significant impact, these errors and their impact do not come as a surprise if the quality of the GPS/INS integration process is carefully examined. Therefore, the mounting errors are singled out in this work. The ideal solution for improving the compatibility of neighbouring strips in the presence of errors in the mounting parameters is the implementation of a rigorous calibration procedure. However, such a calibration requires the original observations, which may not be usually available. In this paper, a strip adjustment procedure to improve the compatibility between parallel lidar strips with moderate flight dynamics (for example, acquired by a fixed-wing aircraft) over an area with moderately varying elevation is proposed. The proposed method is similar to the photogrammetric block adjustment of independent models. Instead of point features, planar patches and linear features, which are represented by sets of non-conjugate points, are used for the strip adjustment. The feasibility and the performance of the proposed procedure together with its impact on subsequent activities are illustrated using experimental results from real data. [ABSTRACT FROM AUTHOR]

Stockpile quantity monitoring is vital for agencies and businesses to maintain inventory of bulk material such as salt, sand, aggregate, lime, and many other materials commonly used in agriculture, highways, and industrial applications. Traditional approaches for volumetric assessment of bulk material stockpiles, e.g., truckload counting, are inaccurate and prone to cumulative errors over long time. Modern aerial and terrestrial remote sensing platforms equipped with camera and/or light detection and ranging (LiDAR) units have been increasingly popular for conducting high-fidelity geometric measurements. Current use of these sensing technologies for stockpile volume estimation is impacted by environmental conditions such as lack of global navigation satellite system (GNSS) signals, poor lighting, and/or featureless surfaces. This study addresses these limitations through a new mapping platform denoted as Stockpile Monitoring and Reporting Technology (SMART), which is designed and integrated as a time-efficient, cost-effective stockpile monitoring solution. The novel mapping framework is realized through camera and LiDAR data-fusion that facilitates stockpile volume estimation in challenging environmental conditions. LiDAR point clouds are derived through a sequence of data collections from different scans. In order to handle the sparse nature of the collected data at a given scan, an automated image-aided LiDAR coarse registration technique is developed followed by a new segmentation approach to derive features, which are used for fine registration. The resulting 3D point cloud is subsequently used for accurate volume estimation. Field surveys were conducted on stockpiles of varying size and shape complexity. Independent assessment of stockpile volume using terrestrial laser scanners (TLS) shows that the developed framework had close to 1% relative error. [ABSTRACT FROM AUTHOR]

Forensic crash investigation often requires developing detailed profiles showing the location and extent of vehicle damage to identify impact areas, impact direction, deformation, and estimated vehicle speeds at impact. Traditional damage profiling techniques require extended and comprehensive setups for mapping and measurement that are quite labor- and time-intensive. Due to the time involved, this damage profiling is usually done in a remote holding area after the crash scene is cleared. Light detection and ranging (LiDAR) scanning technology in consumer handheld electronic devices, such as smartphones and tablets, holds significant potential for conducting this damage profile mapping in just a few minutes, allowing the mapping to be conducted at the scene before the vehicle(s) are moved. However, there is limited research and even scarcer published literature on field procedures and/or accuracy for these emerging smartphones and tablets with LiDAR. This paper proposes a methodology and subsequent measurement accuracy comparisons for survey-grade terrestrial laser scanning (TLS) and handheld alternatives. The maximum root mean square error (RMSE) obtained for profile distance between handheld (iPad) and survey-grade TLS LiDAR scans for a damaged vehicle was observed to be 3 cm, a level of accuracy that is likely sufficient and acceptable for most forensic studies. [ABSTRACT FROM AUTHOR]

Collecting precise as-built data is essential for tracking construction progress. Three-dimensional models generated from such data capture the as-is conditions of the structures, providing valuable information for monitoring existing infrastructure over time. As-built data can be acquired using a wide range of remote sensing technologies, among which mobile LiDAR is gaining increasing attention due to its ability to collect high-resolution data over a relatively large area in a short time. The quality of mobile LiDAR data depends not only on the grade of onboard LiDAR scanners but also on the accuracy of direct georeferencing information and system calibration. Consequently, millimeter-level accuracy is difficult to achieve. In this study, the performance of mapping-grade and surveying-grade mobile LiDAR systems for bridge monitoring is evaluated against static laser scanners. Field surveys were conducted over a concrete bridge where grinding was required to achieve desired smoothness. A semi-automated, feature-based fine registration strategy is proposed to compensate for the impact of georeferencing and system calibration errors on mobile LiDAR data. Bridge deck thickness is evaluated using surface segments to minimize the impact of inherent noise in the point cloud. The results show that the two grades of mobile LiDAR delivered thickness estimates that are in agreement with those derived from static laser scanning in the 1 cm range. The mobile LiDAR data acquisition took roughly five minutes without having a significant impact on traffic, while the static laser scanning required more than three hours. [ABSTRACT FROM AUTHOR]

The integration of photogrammetric images and lidar data is becoming a powerful procedure that can be applied in the optimisation of photogrammetric mapping techniques. The complementary nature of lidar and photogrammetric data optimises the performance of many procedures used to extract 3D spatial information from data. For example, photogrammetric imagery enables the accurate extraction of building borders and lidar provides accurate 3D points that give information on the physical surfaces of buildings. These properties demonstrate the usefulness of combining the two types of data to achieve a more robust and complete reconstruction of 3D objects. Photogrammetric procedures require the exterior orientation parameters (EOPs) of the images to extract mapping information. Despite the availability of GPS/INS systems, which greatly assist in direct georeferencing of the imagery, the majority of commercially available photogrammetric systems require control information in order to carry out photogrammetric mapping. Due to improvements in the accuracy of lidar systems in recent years, lidar data is considered a viable source of photogrammetric control. Point features are the principal source of control for photogrammetric triangulation, although linear features and planar patches have also been used. This paper presents a method of georeferencing photogrammetric images using lidar data. The method uses the centroids of rectangular building roofs as control points in the photogrammetric procedure. The centroid of a rectangular building roof derived using lidar data is equivalent to a single control point with 3D coordinates, and can therefore be used in traditional photogrammetric systems. Two photogrammetric experiments were carried out to verify the feasibility of the methodology. The results obtained from these experiments confirm the feasibility of applying the proposed methodology to the georeferencing of photogrammetric images using lidar data. [ABSTRACT FROM AUTHOR]

Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires a reasonably detailed mapping of the ditch profile to identify areas in need of excavation to remove long-term sediment accumulation. This study utilizes high-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) for mapping roadside ditches and performing hydrological analyses. The performance of alternative MLMS units, including an unmanned aerial vehicle, an unmanned ground vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system, is evaluated. Point clouds from all the MLMS units are in agreement within the ±3 cm range for solid surfaces and ±7 cm range for vegetated areas along the vertical direction. The portable backpack system that could be carried by a surveyor or mounted on a vehicle is found to be the most cost-effective method for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground-filtering approach—cloth simulation—is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from the LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data turned out to be very close to the highway cross slope design standards of 2% on driving lanes, 4% on shoulders, and a 6-by-1 slope for ditch lines. [ABSTRACT FROM AUTHOR]

Anthropogenic activities and climate change in coastal areas require continuous monitoring for a better understanding of environmental evolution and for the implementation of protection strategies. Surface moisture is one of the important drivers of coastal variability because it highly affects shoreward sand transport via aeolian processes. Several methods have been explored for measuring surface moisture at different spatiotemporal resolutions, and in recent years, light detection and ranging (LiDAR) technology has been investigated as a remote sensing tool for high-spatiotemporal-resolution moisture detection. The aim of the present study is the assessment of the performance of a permanent terrestrial laser scanner (TLS) with an original setting located on a high position and hourly scanning of a wide beach area stretching from a swash zone to the base of a dune in order to evaluate the soil moisture at a high spatiotemporal resolution. The reflectance of a Riegl-VZ2000 located in Noordwijk on the Dutch coast was used to assess a new calibration curve that allows the estimation of soil moisture. Three days of surveys were conducted to collect ground-truth soil moisture measurements with a time-domain reflectometry (TDR) sensor at 4 cm depth. Each in situ measurement was matched with the closest reflectance measurement provided by the TLS; the data were interpolated using a non-linear least squares method. A calibration curve that allowed the estimation of the soil moisture in the range of 0–30% was assessed; it presented a root-mean-square error (RMSE) of 4.3% and a coefficient of determination (R-square) of 0.86. As an innovative aspect, the calibration curve was tested under different circumstances, including weather conditions and tidal levels. Moreover, the TDR data collected during an independent survey were used to validate the assessed curve. The results show that the permanent TLS is a highly suitable technique for accurately evaluating the surface moisture variations on a wide sandy beach area with a high spatiotemporal resolution. [ABSTRACT FROM AUTHOR]

Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Moreover, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping. • We propose a targetless quality control framework for multi-date UAV LiDAR data. • Row/alley locations can be automatically detected using LiDAR point clouds. • The approach deals with different planting patterns, crop types, and growth stages. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a miniaturized mobile mapping platform with advantages over current agricultural phenotyping systems in terms of acquiring data that facilitate under-canopy plant trait extraction. The system is based on an unmanned ground vehicle (UGV) for in-row, under-canopy data acquisition to deliver accurately georeferenced 2D and 3D products. The paper addresses three main aspects pertaining to the UGV development: (a) architecture of the UGV mobile mapping system (MMS), (b) quality assessment of acquired data in terms of georeferencing information as well as derived 3D point cloud, and (c) ability to derive phenotypic plant traits using data acquired by the UGV MMS. The experimental results from this study demonstrate the ability of the UGV MMS to acquire dense and accurate data over agricultural fields that would facilitate highly accurate plant phenotyping (better than above-canopy platforms such as unmanned aerial systems and high-clearance tractors). Plant centers and plant count with an accuracy in the 90% range have been achieved. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are quickly emerging as a popular platform for 3D reconstruction/modeling in various applications such as precision agriculture, coastal monitoring, and emergency management. For such applications, LiDAR and frame cameras are the two most commonly used sensors for 3D mapping of the object space. For example, point clouds for the area of interest can be directly derived from LiDAR sensors onboard UAVs equipped with integrated global navigation satellite systems and inertial navigation systems (GNSS/INS). Imagery-based mapping, on the other hand, is considered to be a cost-effective and practical option and is often conducted by generating point clouds and orthophotos using structure from motion (SfM) techniques. Mapping with photogrammetric approaches requires accurate camera interior orientation parameters (IOPs), especially when direct georeferencing is utilized. Most state-of-the-art approaches for determining/refining camera IOPs depend on ground control points (GCPs). However, establishing GCPs is expensive and labor-intensive, and more importantly, the distribution and number of GCPs are usually less than optimal to provide adequate control for determining and/or refining camera IOPs. Moreover, consumer-grade cameras with unstable IOPs have been widely used for mapping applications. Therefore, in such scenarios, where frequent camera calibration or IOP refinement is required, GCP-based approaches are impractical. To eliminate the need for GCPs, this study uses LiDAR data as a reference surface to perform in situ refinement of camera IOPs. The proposed refinement strategy is conducted in three main steps. An image-based sparse point cloud is first generated via a GNSS/INS-assisted SfM strategy. Then, LiDAR points corresponding to the resultant image-based sparse point cloud are identified through an iterative plane fitting approach and are referred to as LiDAR control points (LCPs). Finally, IOPs of the utilized camera are refined through a GNSS/INS-assisted bundle adjustment procedure using LCPs. Seven datasets over two study sites with a variety of geomorphic features are used to evaluate the performance of the developed strategy. The results illustrate the ability of the proposed approach to achieve an object space absolute accuracy of 3–5 cm (i.e., 5–10 times the ground sampling distance) at a 41 m flying height. [ABSTRACT FROM AUTHOR]

Building boundaries play an essential role in many applications such as urban planning and production of 3D realistic views. In this context, airborne LiDAR data have been explored for the generation of digital building models. Despite the many developed strategies, there is no method capable of encompassing all the complexities in an urban environment. In general, the vast majority of existing regularization methods are based on building boundaries that are made up of straight lines. Therefore, the development of a strategy able to model building boundaries, regardless of their degree of complexity is of high importance. To overcome the limitations of existing strategies, an iterative CD-spline (changeable degree spline) regularization method is proposed. The main contribution is the automated selection of the polynomial function that best models each segment of the building roof boundaries. Conducted experiments with real data verified the ability of the proposed approach in modeling boundaries with different levels of complexities, including buildings composed of complex curved segments and point cloud with different densities, presenting Fscore and PoLiS around 95% and 0.30 m, respectively. [ABSTRACT FROM AUTHOR]

Lane markings are one of the essential elements of road information, which is useful for a wide range of transportation applications. Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging (LiDAR) point clouds acquired by mobile mapping systems (MMS). This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction. For comparative evaluation, original intensity thresholding and deep learning using manually established labels strategies are also implemented. A pavement surface-based assessment of lane marking extraction by the four strategies is conducted in asphalt and concrete pavement areas covered by MMS equipped with multiple LiDAR scanners. Additionally, the extracted lane markings are used for lane width estimation and reporting lane marking gaps along various highways. The normalized intensity thresholding leads to a better lane marking extraction with an F1-score of 78.9% in comparison to the original intensity thresholding with an F1-score of 72.3%. On the other hand, the deep learning model trained with automatically generated labels achieves a higher F1-score of 85.9% than the one trained on manually established labels with an F1-score of 75.1%. In concrete pavement area, the normalized intensity thresholding and both deep learning strategies obtain better lane marking extraction (i.e., lane markings along longer segments of the highway have been extracted) than the original intensity thresholding approach. For the lane width results, more estimates are observed, especially in areas with poor edge lane marking, using the two deep learning models when compared with the intensity thresholding strategies due to the higher recall rates for the former. The outcome of the proposed strategies is used to develop a framework for reporting lane marking gap regions, which can be subsequently visualized in RGB imagery to identify their cause. [ABSTRACT FROM AUTHOR]

LiDAR-based mobile mapping systems (MMS) are rapidly gaining popularity for a multitude of applications due to their ability to provide complete and accurate 3D point clouds for any and every scene of interest. However, an accurate calibration technique for such systems is needed in order to unleash their full potential. In this paper, we propose a fully automated profile-based strategy for the calibration of LiDAR-based MMS. The proposed technique is validated by comparing its accuracy against the expected point positioning accuracy for the point cloud based on the used sensors' specifications. The proposed strategy was seen to reduce the misalignment between different tracks from approximately 2 to 3 m before calibration down to less than 2 cm after calibration for airborne as well as terrestrial mobile LiDAR mapping systems. In other words, the proposed calibration strategy can converge to correct estimates of mounting parameters, even in cases where the initial estimates are significantly different from the true values. Furthermore, the results from the proposed strategy are also verified by comparing them to those from an existing manually-assisted feature-based calibration strategy. The major contribution of the proposed strategy is its ability to conduct the calibration of airborne and wheel-based mobile systems without any requirement for specially designed targets or features in the surrounding environment. The above claims are validated using experimental results conducted for three different MMS – two airborne and one terrestrial – with one or more LiDAR unit. [ABSTRACT FROM AUTHOR]

Mechanically Stabilized Earth (MSE) walls retain soil on steep, unstable slopes with crest loads. Over the last decade, they are becoming quite popular due to their high cost-to-benefit ratio, design flexibility, and ease of construction. Like any civil infrastructure, MSE walls need to be continuously monitored according to transportation asset management criteria during and after the construction stage to ensure that their expected serviceability measures are met and to detect design and/or construction issues, which could lead to structural failure. Current approaches for monitoring MSE walls are mostly qualitative (e.g., visual inspection or examination). Besides being time consuming, visual inspection might have inconsistencies due to human subjectivity. This research focuses on a comprehensive strategy using a mobile LiDAR mapping System (MLS) for the acquisition and processing of point clouds covering the MSE wall. The processing strategy delivers a set of global and local performance measure for MSE walls. Moreover, it is also capable of handling MSE walls with smooth or textured panels with the latter being the focus of this research due to its more challenging nature. For this study, an ultra-high-accuracy wheel-based MLS has been developed to efficiently acquire reliable data conducive to the development of the serviceability measures. To illustrate the feasibility of the proposed acquisition/processing strategy, two case studies in this research have been conducted with the first one focusing on the comparative performance of static and mobile LiDAR in terms of the agreement of the derived serviceability measures. The second case study aims at illustrating the feasibility of the proposed strategy in handling large textured MSE walls. Results from both case studies confirm the potential of using MLS for efficient, economic, and reliable monitoring of MSE walls. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV)-based remote sensing techniques have demonstrated great potential for monitoring rapid shoreline changes. With image-based approaches utilizing Structure from Motion (SfM), high-resolution Digital Surface Models (DSM), and orthophotos can be generated efficiently using UAV imagery. However, image-based mapping yields relatively poor results in low textured areas as compared to those from LiDAR. This study demonstrates the applicability of UAV LiDAR for mapping coastal environments. A custom-built UAV-based mobile mapping system is used to simultaneously collect LiDAR and imagery data. The quality of LiDAR, as well as image-based point clouds, are investigated and compared over different geomorphic environments in terms of their point density, relative and absolute accuracy, and area coverage. The results suggest that both UAV LiDAR and image-based techniques provide high-resolution and high-quality topographic data, and the point clouds generated by both techniques are compatible within a 5 to 10 cm range. UAV LiDAR has a clear advantage in terms of large and uniform ground coverage over different geomorphic environments, higher point density, and ability to penetrate through vegetation to capture points below the canopy. Furthermore, UAV LiDAR-based data acquisitions are assessed for their applicability in monitoring shoreline changes over two actively eroding sandy beaches along southern Lake Michigan, Dune Acres, and Beverly Shores, through repeated field surveys. The results indicate a considerable volume loss and ridge point retreat over an extended period of one year (May 2018 to May 2019) as well as a short storm-induced period of one month (November 2018 to December 2018). The foredune ridge recession ranges from 0 m to 9 m. The average volume loss at Dune Acres is 18.2 cubic meters per meter and 12.2 cubic meters per meter within the one-year period and storm-induced period, respectively, highlighting the importance of episodic events in coastline changes. The average volume loss at Beverly Shores is 2.8 cubic meters per meter and 2.6 cubic meters per meter within the survey period and storm-induced period, respectively. [ABSTRACT FROM AUTHOR]

Recent advances in sensor and algorithmic technologies have led to the exploration of remote/proximal sensing for automated forest inventory at various scales. For detailed below-canopy mapping to derive critical forest biometrics, such as diameter at breast height (DBH), merchantable height, and debris volume, under-canopy mobile LiDAR mapping is preferred. These mapping systems typically rely on an onboard integrated global navigation satellite system/inertial navigation system (GNSS/INS) unit for point cloud generation. The main challenge of such under-canopy mapping is the intermittent access to the GNSS signal, which is crucial to deriving accurately georeferenced mapping products. Advances in Simultaneous Localization and Mapping (SLAM) offer an alternative to GNSS/INS-assisted georeferencing in GNSS-denied/challenging scenarios. In this study, we propose a general and comprehensive Forest Feature LiDAR SLAM framework that encompasses odometry and mapping threads for a 3D LiDAR unit mounted on backpack systems to achieve accurate forest inventories. In the odometry thread, ground/tree trunk features are extracted from LiDAR scans (i.e., points from a full revolution of the laser beams) and used to estimate the transformation between successive scans. The mapping thread performs local/global least squares adjustment (LSA) using derived features to register LiDAR scans to a common reference frame. One advantage of the proposed framework is that the tree trunk features and ground information – which are critical to forest inventory applications – are directly derived in the SLAM process, making it superior to other geometric feature-based approaches. Additionally, relative trajectory information provided by onboard navigation units and/or reference point clouds from other sources can be incorporated into the process. In this study, three in-house developed backpack systems with varying specifications were used to collect data in complex forest areas. The proposed SLAM strategy was performed on these datasets and compared with point clouds acquired by uncrewed aerial vehicle (UAV) and a commercial backpack LiDAR system. Experimental results suggested that the proposed framework can produce point clouds with satisfactory intra-dataset alignment quality (in the range of 2–4 cm) and positional accuracy (around 10 cm) for all backpack systems. [ABSTRACT FROM AUTHOR]

[Display omitted] Unmanned aerial vehicles (UAVs) equipped with integrated global navigation satellite systems/inertial navigation systems (GNSS/INS) together with cameras and/or LiDAR sensors are being widely used for topographic mapping in a variety of applications such as precision agriculture, coastal monitoring, and archaeological documentation. Integration of image-based and LiDAR point clouds can provide a comprehensive 3D model of the area of interest. For such integration, ensuring a good alignment between data from the different sources is critical. Although many works have been conducted on this topic, there is still a need for a rigorous integration approach that minimizes the discrepancy between camera and LiDAR data caused by inaccurate system calibration parameters and/or trajectory artifacts. This study proposes an automated tightly-coupled camera/LiDAR integration workflow for GNSS/INS-assisted UAV systems. The proposed strategy is conducted in three main steps. First, an image-based point cloud is generated using a LiDAR/GNSS/INS-assisted structure from motion (SfM) strategy. Then, feature correspondences between image-based and LiDAR point clouds are automatically identified. Finally, an integrated-bundle adjustment procedure including image points, LiDAR raw measurements, and GNSS/INS information is conducted to minimize the discrepancy between point clouds from different sensors while estimating system calibration parameters and refining the trajectory information. The proposed SfM strategy and integration framework are evaluated using five datasets. The SfM results show that using LiDAR data can facilitate feature matching and further increase the number of reconstructed 3D points. The experimental results also illustrate that the developed automated camera/LiDAR integration strategy is capable of accurately estimating system calibration parameters to achieve good alignment among camera/LiDAR data from single/multiple systems. An absolute accuracy in the range of 3–5 cm is achieved for the image/LiDAR point clouds after the integration process. [ABSTRACT FROM AUTHOR]

Image spectral and Light Detection and Ranging (LiDAR) positional information can be related through the orthophoto generation process. Orthophotos have a uniform scale and represent all objects in their correct planimetric locations. However, orthophotos generated using conventional methods suffer from an artifact known as the double-mapping effect that occurs in areas occluded by tall objects. The double-mapping problem can be resolved through the commonly known true orthophoto generation procedure, in which an occlusion detection process is incorporated. This paper presents a review of occlusion detection methods, from which three techniques are compared and analyzed using experimental results. The paper also describes a framework for true orthophoto production based on an angle-based occlusion detection method. To improve the performance of the angle-based technique, two modifications to this method are introduced. These modifications, which aim at resolving false visibilities reported within the angle-based occlusion detection process, are referred to as occlusion extension and radial section overlap. A weighted averaging approach is also proposed to mitigate the seamline effect and spectral dissimilarity that may appear in true orthophoto mosaics. Moreover, true orthophotos generated from high-resolution aerial images and high-density LiDAR data using the updated version of angle-based methodology are illustrated for two urban study areas. To investigate the potential of image matching techniques in producing true orthophotos and point clouds, a comparison between the LiDAR-based and image-matching-based true orthophotos and digital surface models (DSMs) for an urban study area is also presented in this paper. Among the investigated occlusion detection methods, the angle-based technique demonstrated a better performance in terms of output and running time. The LiDAR-based true orthophotos and DSMs showed higher qualities compared to their image-matching-based counterparts which contain artifacts/noise along building edges. [ABSTRACT FROM AUTHOR]

Abstract: Airborne light detection and ranging (LiDAR) systems are used to measure the range (distance from the sensor to the target) and the intensity data (the backscattered energy from the target). LiDAR has been used extensively to model the topography of the Earth surface. Nowadays, LiDAR systems operating in the near-infrared spectral range are also gaining high interest for land cover classification and object recognition. LiDAR system requires geometric calibration (GC) and radiometric correction (RC) in order to maximize the benefit from the collected LiDAR data. This paper evaluates the impact of the GC and the RC of the LiDAR data on land cover classification. The procedure includes the use of a quasi-rigorous method for the GC and the radar (range) equation for the RC of the LiDAR data. The geometric calibration procedure is used to adjust the coordinates of the point cloud by removing the impact of biases in the system parameters as well as deriving corrected ranges and scan angles (in the absence of the system’s raw measurements) for the RC process. The geometrically calibrated ranges and scan angles are then used to correct the intensity data from the atmospheric attenuation and background backscattering based on the radar (range) equation. The atmospheric attenuation, which has not been fully addressed in the previous literature, is modeled by considering the parameters of absorption as well as scattering derived from existing empirical models and public (free) molecular absorption database. A LiDAR dataset covering an urban area is used to evaluate the effect of the GC and RC of the LiDAR data on land cover classification. The results are evaluated using a true ortho-rectified aerial image acquired during the same flight mission. The classification results show an accuracy improvement of about 9.4–12.8% for the LiDAR data used after the GC and RC. The study provides a practical approach for the LiDAR system GC and RC which can be implemented by most of the data end users. The outcome from this research work is a data processing tool that maximizes the benefits of using the intensity data for object recognition and land cover classification, which will be quite important for LiDAR data users. [Copyright &y& Elsevier]

Accurate registration of light detection and ranging (LiDAR) point clouds and images is a prerequisite for integrating the spectral and geometrical information collected by low-cost unmanned aerial vehicle (UAV) systems. Most registration approaches take the directly georeferenced LiDAR point cloud as a rigid body, based on the assumption that the high-precision positioning and orientation system (POS) in the LiDAR system provides sufficient precision, and that the POS errors are negligible. However, due to the large errors of the low-precision POSs commonly used in the low-cost UAV LiDAR systems (ULSs), dramatic deformation may exist in the directly georeferenced ULS point cloud, resulting in non-rigid transformation between the images and the deformed ULS point cloud. As a result, registration may fail when using a rigid transformation between the images and the directly georeferenced LiDAR point clouds. To address this problem, we proposed NRLI-UAV, which is a non-rigid registration method for registration of sequential raw laser scans and images collected by low-cost UAV systems. NRLI-UAV is a two-step registration method that exploits trajectory correction and discrepancy minimization between the depths derived from structure from motion (SfM) and the raw laser scans to achieve LiDAR point cloud quality improvement. Firstly, the coarse registration procedure utilizes global navigation satellite system (GNSS) and inertial measurement unit (IMU)-aided SfM to obtain accurate image orientation and corrects the errors of the low-precision POS. Secondly, the fine registration procedure transforms the original 2D-3D registration to 3D-3D registration. This is performed by setting the oriented images as the reference, and iteratively minimizing the discrepancy between the depth maps derived from SfM and the raw laser scans, resulting in accurate registration between the images and the LiDAR point clouds. In addition, an improved LiDAR point cloud is generated in the mapping frame. Experiments were conducted with data collected by a low-cost UAV system in three challenging scenes to evaluate NRLI-UAV. The final registration errors of the images and the LiDAR point cloud are less than one pixel in image space and less than 0.13 m in object space. The LiDAR point cloud quality was also evaluated by plane fitting, and the results show that the LiDAR point cloud quality is improved by 8.8 times from 0.45 m (root-mean-square error [RMSE] of plane fitting) to 0.05 m (RMSE of plane fitting) using NRLI-UAV, demonstrating a high level of automation, robustness, and accuracy. [ABSTRACT FROM AUTHOR]

The recent record-breaking Laurentian Great Lakes water levels have led to severe erosion along many shorelines, but quantification of this erosion has not yet been widely reported. In this paper, novel shoreline measurements of Lake Michigan erosion, using a LiDAR-equipped unmanned aerial vehicle (UAV). The vehicle surveyed two beaches, Dune Acres and Beverly Shores, along the Indiana shoreline of southern Lake Michigan over the period 2018–2019, which are compared to existing aircraft-based LiDAR surveys. The UAV surveys reveal extensive shoreline erosion and recession at both beaches since the low water conditions of 2012–2013, with both beaches losing 70.7 and 64.8 m3/m of dry beach volume. Shoreline recession over this period was nearly 35 m for both beaches. In the period 2018–2019, the UAV measurements reveal continued beach erosion and the transition of beach topography to a steep, actively eroding foredune. Storm erosion resolved by short-interval surveys highlight the episodic nature of shoreline erosion, with 8 m3/m of material lost over a stormy two-week period at Dune Acres. While large, the erosion and recession observed at both beaches is in keeping with limited historical observations of shoreline change during high water episodes. However, the observed overall shoreline recession slopes of 1:29 (water level rise : recession) are much less than historical slopes, suggesting that the beaches are not yet in equilibrium with the high water levels and that erosion will continue in the foreseeable future while lake levels remain high. • A novel LiDAR-equipped unmanned aerial vehicle (UAV) provides timely estimates of shoreline erosion at two Lake Michigan beaches during period of rapid, large water level increase. • Shoreline recession at both beaches between 2013 and 2019 is nearly 35 m, with 70 m3/m of material lost to submergence and erosion. • The presently-observed shoreline recession slope of 1:29 suggests that at the time of the 2019 surveys both beaches were not yet in equilibrium with the elevated water levels. • While the observed erosion is significant, it is consistent with Lake Michigan beach erosion during similar periods of water level increase. [ABSTRACT FROM AUTHOR]